Fecal metabolomics: assay performance and association with colorectal cancer

Machine learning-assisted label-free colorectal cancer diagnosis using plasmonic needle-endoscopy system

Early and accurate detection of colorectal cancer (CRC) is critical for improving patient outcomes. Existing diagnostic techniques are often invasive and carry risks of complications. Herein, we introduce a plasmonic gold nanopolyhedron (AuNH)-coated needle-based surface-enhanced Raman scattering (SERS) sensor, integrated with endoscopy, for direct mucus sampling and label-free detection of CRC. The thin and flexible stainless-steel needle is coated with polymerized dopamine, which serves as an adhesive layer and simultaneously initiates the nucleation of gold nanoparticle (AuNP) seeds on the needle surface. The AuNP seeds are further grown through a surface-directed reduction using Au ions-hydroxylamine hydrochloride solution, resulting in the formation of dense AuNHs. The formation mechanism of AuNHs and the layered structure of the plasmonic needle-based SERS (PNS) sensor are thoroughly analyzed. Furthermore, a strong field enhancement of the PNS sensor is observed, amplified around the edges of the polyhedral shapes and at nanogap sites between AuNHs. The feasibility of the PNS sensor combined with endoscopy system is further investigated using mouse models for direct colonic mucus sampling and verifying noninvasive label-free classification of CRC from normal controls. A logistic regression-based machine learning method is employed and successfully differentiates CRC and normal mice, achieving 100% sensitivity, 93.33% specificity, and 96.67% accuracy. Moreover, Raman profiling of metabolites and their correlations with Raman signals of mucus samples are analyzed using the Pearson correlation coefficient, offering insights for identifying potential cancer biomarkers. The developed PNS-assisted endoscopy technology is expected to advance the early screening and diagnosis approach of CRC in the future.

Interactions between diet and gut microbiota in cancer

Dietary patterns and specific dietary components, in concert with the gut microbiota, can jointly shape susceptibility, resistance and therapeutic response to cancer. Which diet-microbial interactions contribute to or mitigate carcinogenesis and how they work are important questions in this growing field. Here we interpret studies of diet-microbial interactions to assess dietary determinants of intestinal colonization by opportunistic and oncogenic bacteria. We explore how diet-induced expansion of specific gut bacteria might drive colonic epithelial tumorigenesis or create immuno-permissive tumour milieus and introduce recent findings that provide insight into these processes. Additionally, we describe available preclinical models that are widely used to study diet, microbiome and cancer interactions. Given the rising clinical interest in dietary modulations in cancer treatment, we highlight promising clinical trials that describe the effects of different dietary alterations on the microbiome and cancer outcomes.

Effects of pork sausage on intestinal microecology and metabolism in mice

BACKGROUND

Processed meat, as an important part of the human diet, has been recognized as a carcinogen by the International Agency for Research on Cancer (IARC). Although numerous epidemiological reports supported the IARC's view, the relevant evidence of a direct association between processed meat and carcinogenicity has been insufficient and the mechanism has been unclear. This study aims to investigate the effects of pork sausage (as a representative example of processed meat) intake on gut microbial communities and metabolites of mice. Microbial communities and metabolites from all groups were analyzed using 16S rRNA gene sequencing and Ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometer (UPLC-Q-TOF/MS), respectively.

RESULTS

The levels of Bacteroidetes, Bacteroides, Alloprevotella, Lactobacillus, Prevotella_9, Lachnospiraceae_NK4A136_group, Alistipes, Blautia, Proteobacteria, Firmicutes, Allobaculum, Helicobacter, Desulfovibrio, Clostridium_sensu_stricto_1, Ruminococcaceae_UCG-014, Lachnospiraceae_UCG-006 and Streptococcus (P < 0.05) were obviously altered in the mice fed a pork sausage diet. Twenty-seven metabolites from intestinal content samples and fourteen matabolites from whole blood samples were identified as potential biomarkers from multivariate analysis, including Phosphatidic acid (PA), Sphingomyelin (SM), Lysophosphatidylcholine (LysoPC), Diglyceride (DG), D-maltose, N-acylamides and so forth. The significant changes in these biomarkers demonstrate metabonomic variations in pork sausage treated rats, especially carbohydrate metabolism, lipid metabolism, and amino acid metabolism.

CONCLUSION

The present study provided evidence that a processed meat diet can increase the risk of colorectal cancer and other diseases significantly by altering the microbial community structure and disrupting the body's metabolic pathways. © 2023 Society of Chemical Industry.

Children with Hirschsprung disease exhibited alterations in host-microbial co-metabolism after pull-through operation

PURPOSE

This study aims to compare the fecal metabolome in post pull-through HD with and without HAEC patients and healthy young children using nuclear magnetic resonance (NMR) spectroscopy.

METHODS

Fresh fecal samples were collected from children under 5 years of age in both post-pull-through HD patients and healthy Thai children. A total of 20 fecal samples were then analyzed using NMR spectroscopy.

RESULTS

Thirty-four metabolites identified among HD and healthy children younger than 5 years were compared. HD samples demonstrated a significant decrease in acetoin, phenylacetylglutamine, and N-acetylornithine (corrected p value = 0.01, 0.04, and 0.004, respectively). Succinate and xylose significantly decreased in HD with HAEC group compared to HD without HAEC group (corrected p value = 0.04 and 0.02, respectively). Moreover, glutamine and glutamate metabolism, and alanine, aspartate, and glutamate metabolism were the significant pathways involved, with pathway impact 0.42 and 0.50, respectively (corrected p value = 0.02 and 0.04, respectively).

CONCLUSION

Differences in class, quantity, and metabolism of protein and other metabolites in young children with HD after pull-through operation were identified. Most of the associated metabolic pathways were correlated with the amino acids metabolism, which is required to maintain intestinal integrity and function.

Structural elucidation of derivatives of polyfunctional metabolites after methyl chloroformate derivatization by high-resolution mass spectrometry gas chromatography. Application to microbiota metabolites

Metabolomics has become an essential discipline in the study of microbiome, emerging gas chromatography coupled to mass spectrometry as the most mature, robust, and reproducible analytical technique. Silylation is the most widely used chemical derivatization strategy, although it has some limitations. In this regard, alkylation by alkyl chloroformate offers some advantages, such as a rapid reaction, milder conditions, better reproducibility, and the generation of more stable derivatives. However, commercial spectral libraries do not include many of the alkyl derivatives, mainly for polyfunctional metabolites, which can form multiple derivatives. That introduces a huge bias in untargeted metabolomics leading to common errors such as duplicates, unknowns, misidentifications, wrong assignations, and incomplete results from which non-reliable findings and conclusions will be retrieved. For this reason, the purpose of this study is to overcome these shortcomings and to expand the knowledge of metabolites in general and especially those closely related to the gut microbiota through the thorough study of the reactivity of the different functional groups in real matrix derivatized by methyl chloroformate, a common representative alkylation reagent. To this end, a systematic workflow has been developed based on exhaustive structural elucidation, along with computational simulation, and taking advantage of the high sensitivity and high-resolution gas chromatography-mass spectrometry. Several empirical rules have been established according to chemically different entities (free fatty acids, amino acids, polyols, sugars, amines, and polyfunctional groups, etc.) to predict the number of derivatives formed from a single metabolite, as well as their elution order and structure. In this work, some methyl chloroformate derivatives not previously reported as well as the mechanisms to explain them are given. Extremely important is the interconversion of E- and Z- geometric isomers of unsaturated dicarboxylic acids (case of fumaric-maleic and case of citraconic-mesaconic acids), or the formation of cycled derivatives for amino acids, as well as common metabolites, as in the case of serine and cysteine, and many others.

Multi-omic integration of microbiome data for identifying disease-associated modules

The human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiome's role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions. Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms. Here, we address this challenge by introducing MintTea - an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease. Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuring Peptostreptococcus and Gemella species and several fecal amino acids, in agreement with these species' reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features. Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.

Metabolomics analyses of cancer tissue from patients with colorectal cancer

The alteration of metabolism is essential for the initiation and progression of numerous types of cancer, including colorectal cancer (CRC). Metabolomics has been used to study CRC. At present, the reprogramming of the metabolism in CRC remains to be fully elucidated. In the present study, comprehensive untargeted metabolomics analysis was performed on the paired CRC tissues and adjacent normal tissues from patients with CRC (n=35) using ultra‑high‑performance liquid chromatography‑mass spectrometry. Subsequently, bioinformatic analysis was performed on the differentially expressed metabolites. The changes in these differential metabolites were compared among groups of patients based on sex, anatomical tumor location, grade of tumor differentiation and stage of disease. A total of 927 metabolites were detected in the tissue samples, and 24 metabolites in the CRC tissue were significantly different compared with the adjacent normal tissue. The present study revealed that the levels of three amino acid metabolites were increased in the CRC tissue, specifically, N‑α‑acetyl‑ε‑(2‑propenal)‑Lys, cyclo(Glu‑Glu) and cyclo(Phe‑Glu). The metabolites with decreased levels in the CRC tissue included quinaldic acid (also referred to as quinoline‑2‑carboxilic acid), 17α‑ and 17β‑estradiol, which are associated with tumor suppression activities, as well as other metabolites such as, anhydro‑β‑glucose, Asp‑Arg, lysophosphatidylcholine, lysophosphatidylethanolamine (lysoPE), lysophosphatidylinositol, carnitine, 5'‑deoxy‑5'‑(methylthio) adenosine, 2'‑deoxyinosine‑5'‑monophosphate and thiamine monophosphate. There was no difference in the levels of the differential metabolites between male and female patients. The differentiation of CRC also showed no impact on the levels of the differential metabolites. The levels of lysoPE were increased in the right side of the colon compared with the left side of the colon and rectum. Analysis of the different tumor stages indicated that 2‑aminobenzenesulfonic acid, P‑sulfanilic acid and quinoline‑4‑carboxylic acid were decreased in stage I CRC tissue compared with stage II, III and IV CRC tissue. The levels of N‑α‑acetyl‑ε‑(2‑propenal)‑Lys, methylcysteine and 5'‑deoxy‑5'‑(methylthio) adenosine varied at different stages of tumorigenesis. These differential metabolites were implicated in multiple metabolism pathways, including carbohydrate, amino acid, lipid, nucleotide and hormone. In conclusion, the present study demonstrated that CRC tumors had altered metabolites compared with normal tissue. The data from the metabolic profile of CRC tissues in the present study provided supportive evidence to understand tumorigenesis.

Suppression of hepatocellular carcinoma by Ulva lactuca ulvan via gut microbiota and metabolite interactions

INTRODUCTION

Ulva lactuca polysaccharide (ULP) is green algae extract with numerous biological activities, including anticoagulant, anti-inflammatory, and antiviral effects. However, the inhibitory ability of ULP in the development of hepatocellular carcinoma warrants further studies.

OBJECTIVES

To elucidate the anti-tumor mechanism of ULP action and evaluate its regulatory effect on gut microbiota and metabolism in H22 hepatocellular carcinoma tumor-bearing mice.

METHODS

An H22 tumor-bearing mouse model was established by subcutaneously injecting H22 hepatoma cells. The gut microbiota composition in cecal feces was assessed and subjected to untargeted metabolomic sequencing. The antitumor activity of ULP was verified further by western blot, RT-qPCR, and reactive oxygen species (ROS) assays.

RESULTS

Administration of ULP alleviated tumor growth by modulating the compositions of the gut microbial communities (Tenericutes, Agathobacter, Ruminiclostridium, Parabacteroides, Lactobacillus, and Holdemania) and metabolites (docosahexaenoic acid, uric acid, N-Oleoyl Dopamine, and L-Kynurenine). Mechanistically, ULP promoted ROS production by inhibiting the protein levels of JNK, c-JUN, PI3K, Akt, and Bcl-6, thereby delaying the growth of HepG2 cells.

CONCLUSION

ULP attenuates tumor growth in H22 tumor-bearing mice by modulating gut microbial composition and metabolism. ULP inhibits tumor growth mainly by promoting ROS generation.

GC-MS Based Metabolomics Analysis to Evaluate Short-Term Effect of Tumor Removal on Patients with Early-Stage Breast Cancer

In this study, it was aimed to demonstrate the short-term effect of breast cancer surgery and tumor removal on the metabolomic profiles of patients with early-stage breast cancer. This cohort consisted of 18 early-stage breast carcinoma patients who had breast cancer surgery to remove tumor and surrounding tissues. The blood samples obtained preoperatively and 24 h after surgery were used in this investigation. Gas chromatography-mass spectrometry (GC-MS) based metabolomic analysis was performed to determine the metabolites. The GC-MS-based metabolomics profile enabled the identification of 162 metabolites in the plasma samples. Postoperatively, glyceric acid, phosphoric acid, O-phosphocolamine, 2-hydroxyethyliminodiacetic acid, N-acetyl-D-mannosamine, N-acetyl-5-hydroxytryptamine, methyl stearate, methyl oleate, iminodiacetic acid, glycerol 1-phosphate, β-glycerol phosphate and aspartic acid were found to be significantly increased (P < 0.05 for all), whereas saccharic acid, leucrose, gluconic acid, citramalic acid and acetol were significantly decreased (P < 0.05 for all). Breast cancer surgery and tumor removal has an impact on the metabolomic profiles of patients with early-stage breast cancer. These findings can be used for understanding the pathogenesis of breast cancer biology and screening the success of the surgery.

Urine and serum metabolic profiling combined with machine learning for autoimmune disease discrimination and classification

Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and minimum sample consumption must be addressed. Herein, we performed metabolomic profiling by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in urine and serum samples. Combined with machine learning (ML), metabolomic patterns from urine achieved the discrimination and classification of ADs with high accuracy. Furthermore, metabolic disturbances among different ADs were also investigated, and provided information of etiology. These results demonstrated that urine metabolic patterns based on MALDI-MS and ML manifest substantial potential in precision medicine.

Gut Microbial and Associated Metabolite Markers for Colorectal Cancer Diagnosis

Globally, colorectal cancer (CRC) is the second most common cause of mortality worldwide. Considerable evidence indicates that dysbiosis of the gut microbial community and its metabolite secretions play a fundamental role in advanced adenoma (ADA) and CRC development and progression. This study is a systematic review that aims to assess the clinical association between gut microbial markers and/or gut and circulating metabolites with ADA and CRC. Five electronic databases were searched by four independent reviewers. Only controlled trials that compared ADA and/or CRC with healthy control (HC) using either untargeted (16s rRNA gene or whole genome sequencing) or targeted (gene-based real-time PCR) identification methods for gut microbiome profile, or untargeted or targeted metabolite profiling approaches from the gut or serum/plasma, were eligible. Three independent reviewers evaluated the quality of the studies using the Cochrane Handbook for Systematic Reviews of Interventions. Twenty-four studies were eligible. We identified strong evidence of two microbial markers Fusobacterium and Porphyromonas for ADA vs. CRC, and nine microbial markers Lachnospiraceae-Lachnoclostridium, Ruminococcaceae-Ruminococcus, Parvimonas spp., Parvimonas micra, Enterobacteriaceae, Fusobacterium spp., Bacteroides, Peptostreptococcus-Peptostreptococcus stomatis, Clostridia spp.-Clostridium hylemonae, Clostridium symbiosum, and Porphyromonas-Porphyromonas asaccharolytica for CRC vs. HC. The remaining metabolite marker evidence between the various groups, including ADA vs. HC, ADA vs. HC, and CRC vs. HC, was not of sufficient quality to support additional findings. The identified gut microbial markers can be used in a panel for diagnosing ADA and/or CRC. Further research in the metabolite markers area is needed to evaluate the possibility to use in diagnostic or prognostic markers for colorectal cancer.

Distinct Signatures of Tumor-Associated Microbiota and Metabolome in Low-Grade vs. High-Grade Dysplastic Colon Polyps: Inference of Their Role in Tumor Initiation and Progression

According to the driver-passenger model for colorectal cancer (CRC), the tumor-associated microbiota is a dynamic ecosystem of bacterial species where bacteria with carcinogenic features linked to CRC initiation are defined as "drivers", while opportunistic bacteria colonizing more advanced tumor stages are known as "passengers". We reasoned that also gut microbiota-associated metabolites may be differentially enriched according to tumor stage, and be potential determinants of CRC development. Thus, we characterized the mucosa- and lumen-associated microbiota (MAM and LAM, respectively) and mucosa-associated metabolites in low- vs. high-grade dysplastic colon polyps from 78 patients. We show that MAM, obtained with a new biopsy-preserving approach, and LAM differ in composition and α/β-diversity. By stratifying patients for polyp histology, we found that bacteria proposed as passengers by previous studies colonized high-grade dysplastic adenomas, whereas driver taxa were enriched in low-grade polyps. Furthermore, we report altered "mucosa-associated metabolite" levels in low- vs. high-grade groups. Integrated microbiota-metabolome analysis suggests the involvement of the gut microbiota in the production and consumption of these metabolites. Altogether, our findings support the involvement of bacterial species and associated metabolites in CRC mucosal homeostasis in a tumor-stage-specific manner. These distinct signatures may be used to distinguish low-grade from high-grade dysplastic polyps.

Interaction among biofilter microbiome, fecal metabolome and water quality and regulation of sewage discharge in the recirculating aquaculture system of Apostichopus japonicus

With the aggravation of environmental pollution caused by traditional culture of Apostichopus japonicus, the concept of A. japonicus recirculating aquaculture system (RAS) came into being. To plan the sewage discharge time reasonably, we explored the temporal variation of water quality, biofilter microbe and fecal metabolome in RAS and relevant mechanism. The results showed that monitored water quality in RAS were within the safe living range of A. japonicus. Proteobacteria and Desulfobacterota were dominant bacteria in biofilter. The RDA results and correlation heatmap showed that NH₄-N and NO₂-N significantly affected the microbial community composition. The expression pattern of fecal metabolites changed with the passage of time after feeding. And ROC curve analysis and VIP bar chart showed that there were inter group biomarkers with predictive performance, which could help to remind timely sewage discharge. Topological analysis of KEGG pathway enrichment showed that metabolic pathways such as alanine, aspartate and glutamate metabolism changed significantly after feeding (P < 0.01). Additionally, the correlation analysis results showed that biofilter microbe and fecal metabolites were related to water quality (P < 0.05). Combined with the above research results, this study concluded that the RAS could discharge sewage 25-30 h after feeding. These findings were of direct significance to the management of RAS environment and the protection of A. japonicus healthy growth.

Environmental phenol exposure associates with urine metabolome alteration in young Northeast Indian females

Urinary biomonitoring delivers the most accurate environmental phenols exposure assessment. However, environmental phenol exposure-related biomarkers are required to improve risk assessment to understand the internal processes perturbed, which may link exposure to specific health outcomes. This study aimed to investigate the association between environmental phenols exposure and the metabolome of young adult females from India. Urinary metabolomics was performed using liquid chromatography-mass spectrometry. Environmental phenols-related metabolic biomarkers were investigated by comparing the low and high exposure of environmental phenols. Seven potential biomarkers, namely histidine, cysteine-s-sulfate, 12-KETE, malonic acid, p-hydroxybenzoic acid, PE (36:2), and PS (36:0), were identified, revealing that environmental phenol exposure altered the metabolic pathways such as histidine metabolism, beta-Alanine metabolism, glycerophospholipid metabolism, and other pathways. This study also conceived an innovative strategy for the early prediction of diseases by combining urinary metabolomics with machine learning (ML) algorithms. The differential metabolites predictive accuracy by ML models was >80%. This is the first mass spectrometry-based metabolomics study on young adult females from India with environmental phenols exposure. The study is valuable in demonstrating multiple urine metabolic changes linked to environmental phenol exposure and a better understanding of the mechanisms behind environmental phenol-induced effects in young female adults.

Plasma metabolomics study in screening and differential diagnosis of multiple primary lung cancer

BACKGROUND

Multiple primary lung cancer (MPLC) is becoming increasingly common in clinical practice. Imaging examination is sometimes difficult to differentiate from intrapulmonary metastasis (IM) or single primary lung cancer (SPLC) before surgery. There is a lack of effective blood biomarkers as an auxiliary diagnostic method.

PARTICIPANTS AND METHODS

A total of 179 patients who were hospitalized and operated in our department from January to June 2019 were collected, and they were divided into SPLC with 136 patients, MPLC with 24 patients, and IM with 19 patients. In total, 96 healthy people without lung cancer were enrolled. Medical history, imaging, and pathology data were assembled from all participants. Plasma metabolomics analysis was performed by quadrupole time-of-flight tandem mass spectrometry, and data were analyzed using SPSS19.0/Simca 14.1/MetaboAnalyst5.0 software. Significant metabolites were selected by variable importance in projection, P value, and fold change. The area under the receiver operating characteristic curve was used to evaluate their diagnostic ability.

RESULTS

There were significant differences in plasma metabolite profiles between IM and MPLC. Seven metabolites were screened out. Two metabolites had higher levels in IM, and five metabolites had higher levels in MPLC. All had favorable discriminating capacity. Phosphatidyl ethanolamine (38:5) showed the highest sensitivity (0.95) and specificity (0.92). It was followed by l -histidine with sensitivity 0.92 and specificity 0.84. l -tyrosine can be used to identify SPLC and MPLC. The panel composed of related metabolites exhibited higher diagnostic ability. Eight principal metabolites caused remarkable differences between healthy people and MPLC, and five of them had area under the curves greater than 0.85, showing good discriminating power.

CONCLUSION

Through the study of plasma metabolomics, it was found that there were obvious differences in the metabolite profiles of MPLC, IM, SPLC, and the healthy population. Some discovered metabolites possessed excellent diagnostic competence with high sensitivity and specificity. They had the potential to act as biomarkers for the screening and differential diagnosis of MPLCs.

Progress of gut microbiome and its metabolomics in early screening of colorectal cancer

Gut microbes are widely considered to be closely associated with colorectal cancer (CRC) development. The microbiota is regarded as a potential identifier of CRC, as several studies have found great significant changes in CRC patients' microbiota and metabolic groups. Changes in microbiota, like Fusobacterium nucleatum and Bacteroides fragilis, also alter the metabolic activity of the host, promoting CRC development. In contrast, the metabolome is an intuitive discriminative biomarker as a small molecular bridge to distinguish CRC from healthy individuals due to the direct action of microbes on the host. More diagnostic microbial markers have been found, and the potential discriminatory power of microorganisms in CRC has been investigated through the combined use of biomic genomic metabolomics, bringing new ideas for screening fecal microbial markers. In this paper, we discuss the potential of microorganisms and their metabolites as biomarkers in CRC screening, hoping to provide thoughts and references for non-invasive screening of CRC.

Plasma, urine, and stool metabolites in response to dietary rice bran and navy bean supplementation in adults at high-risk for colorectal cancer

Introduction

Dietary intake of whole grains and legumes and adequate physical activity (PA) have been associated with reduced colorectal cancer (CRC) risk. A single-blinded, two-arm, randomized, placebo-controlled pilot trial was implemented to evaluate the impact of a 12-week dietary intervention of rice bran + navy bean supplementation and PA education on metabolite profiles and the gut microbiome among individuals at high risk of CRC.

Methods

Adults (n=20) were randomized 1:1 to dietary intervention or control. All participants received PA education at baseline. Sixteen study foods were prepared with either heat-stabilized rice bran + navy bean powder or Fibersol®-2 as a placebo. Intervention participants consumed 30 g rice bran + 30 g navy bean powder daily; those in the control group consumed 10 g placebo daily. Non-targeted metabolite profiling was performed by UPLC-MS/MS to evaluate plasma, urine, and stool at 0, 6, and 12 weeks. Stool was also analyzed for primary and secondary bile acids (BAs) and short chain fatty acids (SCFAs) by UPLC-MS/MS and microbial community structure via 16S amplicon sequencing. Two-way ANOVA was used to compare differences between groups for metabolites, and mixed models were used to compare differences between groups for BAs, SCFAs, and alpha and beta diversity measures of microbial community structure.

Results

Across biological matrices, the intervention resulted in changes to several amino acid and lipid metabolites, compared to control. There was a 2.33-fold difference in plasma (p<0.001) and a 3.33-fold difference in urine (p=0.008) for the amino acid S-methylcysteine at 12 weeks. Fold-differences to 4-methoxyphenol sulfate in plasma and urine after 6 and 12 weeks (p<0.001) was a novel result from this combined rice bran and navy bean intervention in people. A 2.98-fold difference in plasma (p=0.002) and a 17.74-fold difference in stool (p=0.026) was observed for the lipid octadecenedioylcarnitine at 12 weeks. For stool BAs, 3-oxocholic acid was increased at 12 weeks compared to control within a subset of individuals (mean difference 16.2 ug/uL, p=0.022). No significant differences were observed between groups for stool SCFAs or microbial community structure.

Discussion

Dietary intake of rice bran + navy beans demonstrates beneficial modulation of host and gut microbial metabolism and represents a practical and affordable means of increasing adherence to national guidelines for CRC control and prevention in a high-risk population.

New metabolic signature for Chagas disease reveals sex steroid perturbation in humans and mice

The causative agent of Chagas disease (CD), Trypanosoma cruzi, claims thousands of lives each year. Current diagnostic tools are insufficient to ensure parasitological detection in chronically infected patients has been achieved. A host-derived metabolic signature able to distinguish CD patients from uninfected individuals and assess antiparasitic treatment efficiency is introduced. Serum samples were collected from chronic CD patients, prior to and three years after treatment, and subjected to untargeted metabolomics analysis against demographically matched CD-negative controls. Five metabolites were confirmed by high-resolution tandem mass spectrometry. Several database matches for sex steroids were significantly altered in CD patients. A murine experiment corroborated sex steroid perturbation in T. cruzi-infected mice, particularly in male animals. Proteomics analysis also found increased steroidogenesis in the testes of infected mice. Metabolic alterations identified in this study shed light on the pathogenesis and provide the basis for developing novel assays for the diagnosis and screening of CD patients.

Integrated Metabonomics and Network Pharmacology to Reveal the Action Mechanism Effect of Shaoyao Decoction on Ulcerative Colitis

Background

Traditional Chinese medicine (TCM) has the advantage of multi-component and multi-target, which becomes a hot spot in the treatment of numerous diseases. Shaoyao decoction (SYD) is a TCM prescription, which is mainly used to treat damp-heat dysentery clinically, with small side effects and low cost. However, its mechanism remains elusive. The purpose of this study is to explore the mechanism of SYD in the treatment of mice with ulcerative colitis (UC) induced by dextran sulfate sodium (DSS) through metabolomics and network pharmacology, and verify through molecular docking and immunohistochemistry, so as to provide a scientific basis for the role of SYD in the treatment of UC.

Materials and Methods

Firstly, DSS-induced UC models were established and then untargeted metabolomics analysis of feces, livers, serum and urine was performed to determine biomarkers and metabolic pathways closely related to the role of SYD. Besides, network pharmacology was applied to screen the active components and UC-related targets, which was verified by molecular docking. Finally, metabonomics and network pharmacology were combined to draw the metabolite-pathway-target network and verified by immunohistochemistry.

Results

Metabolomics results showed that a total of 61 differential metabolites were discovered in SYD-treated UC with 3 main metabolic pathways containing glycerophospholipid metabolism, sphingolipid metabolism and biosynthesis of unsaturated fatty acids, as well as 8 core targets involving STAT3, IL1B, IL6, IL2, AKT1, IL4, ICAM1 and CCND1. Molecular docking demonstrated that the first five targets had strong affinity with quercetin, wogonin, kaempferol and baicalein. Combined with metabolomics and network pharmacology, sphingolipid signaling pathway, PI3K/AKT-mTOR signaling pathway and S1P3 pathway were identified as the main pathways.

Conclusion

SYD can effectively ameliorate various symptoms and alleviate intestinal mucosal damage and metabolic disorder in DSS induced UC mice. Its effect is mainly related to sphingolipid metabolism, PI3K/AKT-mTOR signaling pathway and S1P3 pathway.

Metabolomics Profiling of Nephrotic Syndrome towards Biomarker Discovery

Nephrotic syndrome (NS) is a kidney illness characterized by excessive proteinuria, hypoalbuminemia, edema, and hyperlipidemia, which may lead to kidney failure and necessitate renal transplantation. End-stage renal disease, cardiovascular issues, and mortality are much more common in those with NS. Therefore, the present study aimed to identify potential new biomarkers associated with the pathogenesis and diagnosis of NS. The liquid chromatography–mass spectrometry (LC–MS) metabolomics approach was applied to profile the metabolome of human serum of patients with NS. A total of 176 metabolites were significantly altered in NS compared to the control. Arginine, proline, and tryptophan metabolism; arginine, phenylalanine, tyrosine, and tryptophan biosynthesis were the most common metabolic pathways dysregulated in NS. Furthermore, alanyl-lysine and isoleucyl-threonine had the highest discrimination between NS and healthy groups. The candidate biomarkers may lead to understanding the possible metabolic alterations associated with NS and serve as potential diagnostic biomarkers.

Data-driven identification of plasma metabolite clusters and metabolites of interest for potential detection of early-stage non-small cell lung cancer cases versus cancer-free controls

BACKGROUND

Metabolomics is a potential means for biofluid-based lung cancer detection. We conducted a non-targeted, data-driven assessment of plasma from early-stage non-small cell lung cancer (ES-NSCLC) cases versus cancer-free controls (CFC) to explore and identify the classes of metabolites for further targeted metabolomics biomarker development.

METHODS

Plasma from 250 ES-NSCLC cases and 250 CFCs underwent ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive and negative electrospray ionization (ESI) modes. Molecular feature extraction, formula generation, and find-by-ion tools annotated metabolic entities. Analysis was restricted to endogenous metabolites present in ≥ 80% of samples. Unsupervised hierarchical cluster analysis identified clusters of metabolites. The metabolites with the strongest correlation with the principal component of each cluster were included in logistic regression modeling to assess discriminatory performance with and without adjustment for clinical covariates.

RESULTS

A total of 1900 UHPLC-QTOF-MS assessments identified 1667 and 2032 endogenous metabolites in the ESI-positive and ESI-negative modes, respectively. After data filtration, 676 metabolites remained, and 12 clusters of metabolites were identified from each ESI mode. Multivariable logistic regression using the representative metabolite from each cluster revealed effective classification of cases from controls with overall diagnostic accuracy of 91% (ESI positive) and 94% (ESI negative). Metabolites of interest identified for further targeted analysis include the following: 1b, 3a, 12a-trihydroxy-5b-cholanoic acid, pyridoxamine 5'-phosphate, sphinganine 1-phosphate, gamma-CEHC, 20-carboxy-leukotriene B4, isodesmosine, and 18-hydroxycortisol.

CONCLUSIONS

Plasma-based metabolomic detection of early-stage NSCLC appears feasible. Further metabolomics studies targeting phospholipid, steroid, and fatty acid metabolism are warranted to further develop noninvasive metabolomics-based detection of early-stage NSCLC.

Exploring the mechanism of action of Sanzi formula in intervening colorectal adenoma by targeting intestinal flora and intestinal metabolism

Background

Sanzi formula (SZF) is a kind of Chinese herbal compound that has a certain effect on the prevention and treatment of colorectal adenoma (CRA), which can prevent and control the process of CRA-cancer transformation. In this study, we explored the mechanism of action of SZF in anti-CRA using 16S rRNA sequencing and metabolomics technology.

Methods

Mice were randomly divided into three groups: Control group, Apc^min/+ model group, and SZF treatment group. Except for the Control group, which used C57BL/6 J mice, the remaining two groups used Apc^min/+ mice. The Control group and Apc^min/+ model group were treated with ultrapure water by gavage, while the SZF treatment group was treated with SZF for 12 weeks. During this period, the physical changes of mice in each group were observed. The gut microbiota was determined by high-throughput sequencing of the 16S rRNA gene, and LC-ESI-MS/MS was used for colorectal metabolomics analysis.

Results

Sequencing of the 16S rRNA gut flora yielded 10,256 operational taxonomic units and metabolomic analysis obtained a total of 366 differential metabolites. The intestinal flora analysis showed that SZF could improve intestinal flora disorders in Apc^min/+ mice. For instance, beneficial bacteria such as Gastranaerophilales significantly increased and harmful bacteria such as Angelakisella, Dubosiella, Muribaculum, and Erysipelotrichaceae UCG-003 substantially decreased after the SZF intervention. In addition, metabolomic data analysis demonstrated that SZF also improved the colorectal metabolic profile of Apc^min/+ mice. In Apc^min/+ mice, metabolites such as Anserine and Ectoine were typically increased after SZF intervention; in contrast, metabolites such as Taurocholic acid, Taurochenodesoxycholic acid, Hyocholic acid, Cholic acid, and Tauro-alpha-muricholic acid showed noteworthy reductions. Metabolic flora association analysis indicated that 13 differential flora and 11 differential metabolites were associated.

Conclusion

SZF affects the abundance of specific intestinal flora and regulates intestinal flora disorders, improves colorectal-specific metabolites, and ameliorates intestinal metabolic disorders to prevent and treat CRA. Furthermore, the application of intestinal flora and colorectal metabolomics association analysis offers new strategies to reveal the mechanism of action of herbal medicines for the treatment of intestinal diseases.

Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma

Background

The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases.

Methods

Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified.

Results

In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions.

Conclusion

Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.

Establishment of an early diagnosis model of colon cancerous bowel obstruction based on 1H NMR

Objective

To prospectively establish an early diagnosis model of acute colon cancerous bowel obstruction by applying nuclear magnetic resonance hydrogen spectroscopy(1H NMR) technology based metabolomics methods, combined with machine learning.

Methods

In this study, serum samples of 71 patients with acute bowel obstruction requiring emergency surgery who were admitted to the Emergency Department of Sichuan Provincial People’s Hospital from December 2018 to November 2020 were collected within 2 hours after admission, and NMR spectroscopy data was taken after pretreatment. After postoperative pathological confirmation, they were divided into colon cancerous bowel obstruction (CBO) group and adhesive bowel obstruction (ABO) control group. Used MestReNova software to extract the two sets of spectra bins, and used the MetaboAnalyst5.0 website to perform partial least square discrimination (PLS-DA), combining the human metabolome database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to find possible different Metabolites and related metabolic pathways.

Results

22 patients were classified as CBO group and 30 were classified as ABO control group. Compared with ABO group, the level of Xanthurenic acid, 3-Hydroxyanthranilic acid, Gentisic acid, Salicyluric acid, Ferulic acid, Kynurenic acid, CDP, Mandelic acid, NADPH, FAD, Phenylpyruvate, Allyl isothiocyanate, and Vanillylmandelic acid increased in the CBO group; while the lecel of L-Tryptophan and Bilirubin decreased. There were significant differences between two groups in the tryptophan metabolism, tyrosine metabolism, glutathione metabolism, phenylalanine metabolism and synthesis pathways of phenylalanine, tyrosine and tryptophan (all P<0.05). Tryptophan metabolism pathway had the greatest impact (Impact = 0.19). The early diagnosis model of colon cancerous bowel was established based on the levels of six metabolites: Xanthurenic acid, 3-Hydroxyanthranilic acid, Gentisic acid, Salicylic acid, Ferulic acid and Kynurenic acid (R2 = 0.995, Q2 = 0.931, RMSE = 0.239, AUC = 0.962).

Conclusion

This study firstly used serum to determine the difference in metabolome between patients with colon cancerous bowel obstruction and those with adhesive bowel obstruction. The study found that the metabolic information carried by the serum was sufficient to discriminate the two groups of patients and provided the theoretical supporting for the future using of the more convenient sample for the differential diagnosis of patients with colon cancerous bowel obstruction. Quantitative experiments on a large number of samples were still needed in the future.

Plasma Metabolomics Analysis of Aspirin Treatment and Risk of Colorectal Adenomas

Despite substantial observational and experimental evidence that aspirin use can provide protection against the development of colorectal neoplasia, our understanding of the molecular mechanisms involved is inadequate and limits our ability to use this drug effectively and safely for chemoprevention. We employed an untargeted plasma metabolomics approach using liquid chromatography with high-resolution mass spectroscopy to explore novel metabolites that may contribute to the chemopreventive effects of aspirin. Associations between levels of metabolic features in plasma and aspirin treatment were investigated among 523 participants in a randomized placebo-controlled clinical trial of two doses of aspirin (81 or 325 mg/day) and were linked to risk of colorectal adenoma occurrence over 3 years of follow-up. Metabolic pathways that were altered with aspirin treatment included linoleate and glycerophospholipid metabolism for the 81-mg dose and carnitine shuttle for both doses. Metabolites whose levels increased with 81 mg/day aspirin treatment and were also associated with decreased risk of adenomas during follow-up included certain forms of lysophosphatidylcholine and lysophosphatidylethanolamine as well as trihydroxyoctadecenoic acid, which is a derivative of linoleic acid and is upstream of cyclooxygenase inhibition by aspirin in the linoleate and arachidonic acid metabolism pathways. In conclusion, our findings regarding lysophospholipids and metabolites in the linoleate metabolism pathway may provide novel insights into the chemopreventive effects of aspirin in the colorectum, although they should be considered hypothesis-generating at this time.

PREVENTION RELEVANCE

This research used metabolomics, an innovative discovery-based approach, to identify molecular changes in human blood that may help to explain how aspirin use reduces the risk of colorectal neoplasia in some individuals. Ultimately, this work could have important implications for optimizing aspirin use in the prevention of colorectal cancer.

A deep learning method for predicting metabolite-disease associations via graph neural network

Metabolism is the process by which an organism continuously replaces old substances with new substances. It plays an important role in maintaining human life, body growth and reproduction. More and more researchers have shown that the concentrations of some metabolites in patients are different from those in healthy people. Traditional biological experiments can test some hypotheses and verify their relationships but usually take a considerable amount of time and money. Therefore, it is urgent to develop a new computational method to identify the relationships between metabolites and diseases. In this work, we present a new deep learning algorithm named as graph convolutional network with graph attention network (GCNAT) to predict the potential associations of disease-related metabolites. First, we construct a heterogeneous network based on known metabolite-disease associations, metabolite-metabolite similarities and disease-disease similarities. Metabolite and disease features are encoded and learned through the graph convolutional neural network. Then, a graph attention layer is used to combine the embeddings of multiple convolutional layers, and the corresponding attention coefficients are calculated to assign different weights to the embeddings of each layer. Further, the prediction result is obtained by decoding and scoring the final synthetic embeddings. Finally, GCNAT achieves a reliable area under the receiver operating characteristic curve of 0.95 and the precision-recall curve of 0.405, which are better than the results of existing five state-of-the-art predictive methods in 5-fold cross-validation, and the case studies show that the metabolite-disease correlations predicted by our method can be successfully demonstrated by relevant experiments. We hope that GCNAT could be a useful biomedical research tool for predicting potential metabolite-disease associations in the future.

Integrated analysis of the faecal metagenome and serum metabolome reveals the role of gut microbiome-associated metabolites in the detection of colorectal cancer and adenoma

OBJECTIVE

To profile gut microbiome-associated metabolites in serum and investigate whether these metabolites could distinguish individuals with colorectal cancer (CRC) or adenoma from normal healthy individuals.

DESIGN

Integrated analysis of untargeted serum metabolomics by liquid chromatography-mass spectrometry and metagenome sequencing of paired faecal samples was applied to identify gut microbiome-associated metabolites with significantly altered abundance in patients with CRC and adenoma. The ability of these metabolites to discriminate between CRC and colorectal adenoma was tested by targeted metabolomic analysis. A model based on gut microbiome-associated metabolites was established and evaluated in an independent validation cohort.

RESULTS

In total, 885 serum metabolites were significantly altered in both CRC and adenoma, including eight gut microbiome-associated serum metabolites (GMSM panel) that were reproducibly detected by both targeted and untargeted metabolomics analysis and accurately discriminated CRC and adenoma from normal samples. A GMSM panel-based model to predict CRC and colorectal adenoma yielded an area under the curve (AUC) of 0.98 (95% CI 0.94 to 1.00) in the modelling cohort and an AUC of 0.92 (83.5% sensitivity, 84.9% specificity) in the validation cohort. The GMSM model was significantly superior to the clinical marker carcinoembryonic antigen among samples within the validation cohort (AUC 0.92 vs 0.72) and also showed promising diagnostic accuracy for adenomas (AUC=0.84) and early-stage CRC (AUC=0.93).

CONCLUSION

Gut microbiome reprogramming in patients with CRC is associated with alterations of the serum metabolome, and GMSMs have potential applications for CRC and adenoma detection.

Diet- and microbiota-related metabolite, 5-aminovaleric acid betaine (5-AVAB), in health and disease

5-Aminovaleric acid betaine (5-AVAB) is a trimethylated compound associated with the gut microbiota, potentially produced endogenously, and related to the dietary intake of certain foods such as whole grains. 5-AVAB accumulates within the metabolically active tissues and has been typically found in higher concentrations in the heart, muscle, and brown adipose tissue. Furthermore, 5-AVAB has been associated with positive health effects such as fetal brain development, insulin secretion, and reduced cancer risk. However, it also has been linked with some negative health outcomes such as cardiovascular disease and fatty liver disease. At the cellular level, 5-AVAB can influence cellular energy metabolism by reducing β-oxidation of fatty acids. This review will focus on the metabolic role of 5-AVAB with respect to both physiology and pathology. Moreover, the analytics and origin of 5-AVAB and related compounds will be reviewed.

Metabolomics of Esophageal Squamous Cell Carcinoma Tissues: Potential Biomarkers for Diagnosis and Promising Targets for Therapy

Background

The incidence of esophageal squamous cell carcinoma in China ranks first in the world. The early diagnosis technology is underdeveloped, and the prognosis is poor, which seriously threatens the quality of life of the Chinese people. Epidemiological findings are related to factors such as diet, living habits, and age. The specific mechanism is not clear yet. Metabolomics is a kind of omics that simultaneously and quantitatively analyzes the comprehensive profile of metabolites in living systems. It has unique advantages in the study of the diagnosis and pathogenesis of tumor-related diseases, especially in the search for biomarkers. Therefore, it is desirable to perform metabolic profiling analysis of cancer tissues through metabolomics to find potential biomarkers for the diagnosis and treatment of esophageal squamous cell carcinoma.

Methods

HPLC-TOF-MS/MS technology and Illumina Hiseq Xten Sequencing was used for the analysis of 210 pairs of matched esophageal squamous cell carcinoma tissues and normal tissues in Zhenjiang City, Jiangsu Province, a high-incidence area of esophageal cancer in China. Bioinformatics analysis was also performed.

Results

Through metabolomic and transcriptomic analysis, this study found that a total of 269 differential metabolites were obtained in esophageal squamous cell carcinoma and normal tissues, and 48 differential metabolic pathways were obtained through KEGG enrichment analysis. After further screening and identification, 12 metabolites with potential biomarkers to differentiate esophageal squamous cell carcinoma from normal tissues were obtained.

Conclusions

From the metabolomic data, 4 unknown compounds were found to be abnormally expressed in esophageal squamous cell carcinoma for the first time, such as 9,10-epoxy-12,15-octadecadienoate; 3 metabolites were found in multiple abnormal expression in another tumor, but upregulation or downregulation was found for the first time in esophageal cancer, such as oleoyl glycine; at the same time, it was further confirmed that five metabolites were abnormally expressed in esophageal squamous cell carcinoma, which was similar to the results of other studies, such as PE.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography–tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753–0.9825), 0.7 and 1, respectively.

Integration of Transcriptomics and Metabolomics Reveals the Antitumor Mechanism Underlying Tadalafil in Colorectal Cancer

The potential role of tadalafil, a PDE5 inhibitor, in anticancer activity and prolonged survival has been proposed. However, the systematic effects of tadalafil in colorectal cancer were not fully understood. In this study, we assessed the anti-tumor activity of tadalafil in human colorectal cancer cells. A systematic perspective of the tadalafil-induced anti-tumor mechanism was provided by the integration of transcriptomics and metabolomics. We found that differentially expressed genes (DEGs) were mainly involved in microRNAs in cancer, purine metabolism, glycosphingolipid biosynthesis, arginine biosynthesis, and amino acid metabolism. Amino acid metabolism, especially alanine, aspartate, and glutamate metabolism was the most of the differentially accumulated metabolites (DAMs) through the analysis of metabolomics. The conjoint analysis of DEGs and DAMs presented that they were also mainly involved in alanine, aspartate, and glutamate metabolism. Amino acid metabolism-related genes, GPT, GGT5, and TAT, were significantly decreased after tadalafil treatment. In particular, the disturbance of alanine, aspartate, and glutamate metabolism may be the explanation for the major mechanism resulting from tadalafil anti-tumor activity.

Sturgeon Chondroitin Sulfate Restores the Balance of Gut Microbiota in Colorectal Cancer Bearing Mice

Chondroitin sulfate (CS) is a well-known bioactive substance with multiple biological functions, which can be extracted from animal cartilage or bone. Sturgeon, the largest soft bone animal with ~20% cartilage content, is a great candidate for CS production. Our recent study confirmed the role of sturgeon chondroitin sulfate (SCS) in reducing colorectal cancer cell proliferation and tumor formation. Here, we further studied the effect of SCS on modulating gut microbiome structure in colorectal cancer bearing mice. In this study, the transplanted tumor mice model was constructed to demonstrate that SCS can effectively halt the growth of transplanted colorectal tumor cells. Next, we showed that SCS significantly altered the gut microbiome, such as the abundance of Lactobacillales, Gastranaerophilales, Ruminiclostridiun_5 and Ruminiclostridiun_6. According to linear discriminant analysis (LDA) and abundance map analysis of the microbial metabolic pathways, the changes in microbial abundance led to an increase of certain metabolites (e.g., Phe, Tyr, and Gly). Fecal metabolome results demonstrated that SCS can significantly reduce the amount of certain amino acids such as Phe, Pro, Ala, Tyr and Leu presented in the feces, suggesting that SCS might inhibit colorectal cancer growth by modulating the gut microbiome and altering the production of certain amino acids. Our results revealed the therapeutic potential of SCS to facilitate treatment of colorectal cancer. This study provides insights into the development of novel food-derived therapies for colorectal cancer.

The Application of Metabolomics in Recent Colorectal Cancer Studies: A State-of-the-Art Review

Simple Summary

Colorectal Cancer (CRC) is one of the leading causes of cancer-related death in the United States. Current diagnosis techniques are either highly invasive or lack sensitivity, suggesting the need for alternative techniques for biomarker detection. Metabolomics represents one such technique with great promise in identifying CRC biomarkers with high sensitivity and specificity, but thus far is rarely employed in a clinical setting. In order to provide a framework for future clinical usage, we characterized dysregulated metabolites across recent literature, identifying metabolites dysregulated across a variety of biospecimens. We additionally put special focus on the interplay of the gut microbiome and perturbed metabolites in CRC. We were able to identify many metabolites showing consistent dysregulation in CRC, demonstrating the value of metabolomics as a promising diagnostic technique.

Abstract

Colorectal cancer (CRC) is a highly prevalent disease with poor prognostic outcomes if not diagnosed in early stages. Current diagnosis techniques are either highly invasive or lack sufficient sensitivity. Thus, identifying diagnostic biomarkers of CRC with high sensitivity and specificity is desirable. Metabolomics represents an analytical profiling technique with great promise in identifying such biomarkers and typically represents a close tie with the phenotype of a specific disease. We thus conducted a systematic review of studies reported from January 2012 to July 2021 relating to the detection of CRC biomarkers through metabolomics to provide a collection of knowledge for future diagnostic development. We identified thirty-seven metabolomics studies characterizing CRC, many of which provided metabolites/metabolic profile-based diagnostic models with high sensitivity and specificity. These studies demonstrated that a great number of metabolites can be differentially regulated in CRC patients compared to healthy controls, adenomatous polyps, or across stages of CRC. Among these metabolite biomarkers, especially dysregulated were certain amino acids, fatty acids, and lysophosphatidylcholines. Additionally, we discussed the contribution of the gut bacterial population to pathogenesis of CRC through their modulation to fecal metabolite pools and summarized the established links in the literature between certain microbial genera and altered metabolite levels in CRC patients. Taken together, we conclude that metabolomics presents itself as a promising and effective method of CRC biomarker detection.

A Metabolomics-Based Screening Proposal for Colorectal Cancer

Colorectal cancer (CRC) is a high incidence disease, characterized by high morbidity and mortality rates. Early diagnosis remains challenging because fecal occult blood screening tests have performed sub-optimally, especially due to hemorrhoidal, inflammatory, and vascular diseases, while colonoscopy is invasive and requires a medical setting to be performed. The objective of the present study was to determine if serum metabolomic profiles could be used to develop a novel screening approach for colorectal cancer. Furthermore, the study evaluated the metabolic alterations associated with the disease. Untargeted serum metabolomic profiles were collected from 100 CRC subjects, 50 healthy controls, and 50 individuals with benign colorectal disease. Different machine learning models, as well as an ensemble model based on a voting scheme, were built to discern CRC patients from CTRLs. The ensemble model correctly classified all CRC and CTRL subjects (accuracy = 100%) using a random subset of the cohort as a test set. Relevant metabolites were examined in a metabolite-set enrichment analysis, revealing differences in patients and controls primarily associated with cell glucose metabolism. These results support a potential use of the metabolomic signature as a non-invasive screening tool for CRC. Moreover, metabolic pathway analysis can provide valuable information to enhance understanding of the pathophysiological mechanisms underlying cancer. Further studies with larger cohorts, including blind trials, could potentially validate the reported results.

Evaluation of different stool extraction methods for metabolomics measurements in human faecal samples

Background

Metabolomics analysis of human stool samples is of great interest for a broad range of applications in biomedical research including early detection of colorectal neoplasms. However, due to the complexity of metabolites there is no consensus on how to process samples for stool metabolomics measurements to obtain a broad coverage of hydrophilic and hydrophobic substances.

Methods

We used frozen stool samples (50 mg) from healthy study participants. Stool samples were processed after thawing using eight different processing protocols and different solvents (solvents such as phosphate-buffered saline, isopropanol, methanol, ethanol, acetonitrile and solvent mixtures with or without following evaporation and concentration steps). Metabolites were measured afterwards using the MxP Quant 500 kit (Biocrates). The best performing protocol was subsequently applied to compare stool samples of participants with different dietary habits.

Results

In this study, we were able to determine up to 340 metabolites of various chemical classes extracted from stool samples of healthy study participants with eight different protocols. Polar metabolites such as amino acids could be measured with each method while other metabolite classes, particular lipid species (better with isopropanol and ethanol or methanol following a drying step), are more dependent on the solvent or combination of solvents used. Only a small number of triglycerides or acylcarnitines were detected in human faeces. Extraction efficiency was higher for protocols using isopropanol (131 metabolites>limit of detection (LOD)) or those using ethanol or methanol and methyl tert-butyl ether (MTBE) including an evaporation and concentration step (303 and 342 metabolites>LOD, respectively) than for other protocols. We detected significant faecal metabolite differences between vegetarians, semivegetarians and non-vegetarians.

Conclusion

For the evaluation of metabolites in faecal samples, we found protocols using solvents like isopropanol and those using ethanol or methanol, and MTBE including an evaporation and concentration step to be superior regarding the number of detected metabolites of different chemical classes over others tested in this study.

Age Associated Microbiome and Microbial Metabolites Modulation and Its Association With Systemic Inflammation in a Rhesus Macaque Model

Aging is associated with declining immunity and inflammation as well as alterations in the gut microbiome with a decrease of beneficial microbes and increase in pathogenic ones. The aim of this study was to investigate the age associated gut microbiome in relation to immunologic and metabolic profile in a non-human primate (NHP) model. 12 geriatric (age 19-24 years) and 4 young adult (age 3-4 years) Rhesus macaques were included in this study. Immune cell subsets were characterized in peripheral blood mononuclear cells (PBMC) by flow cytometry and plasma cytokines levels were determined by bead based multiplex cytokine analysis. Stool samples were collected by ileal loop and investigated for microbiome analysis by shotgun metagenomics. Serum, gut microbial lysate, and microbe-free fecal extract were subjected to metabolomic analysis by mass-spectrometry. Our results showed that the gut microbiome in geriatric animals had higher abundance of Archaeal and Proteobacterial species and lower Firmicutes than the young adults. Highly abundant microbes in the geriatric animals showed a direct association with plasma biomarkers of inflammation and immune activation such as neopterin, CRP, TNF, IL-2, IL-6, IL-8 and IFN-γ. Significant enrichment of metabolites that contribute to inflammatory and cytotoxic pathways was observed in serum and feces of geriatric animals compared to the young adults. We conclude that aging NHP undergo immunosenescence and age associated alterations in the gut microbiome that has a distinct metabolic profile. Aging NHP can serve as a model for investigating the relationship of the gut microbiome to particular age-associated comorbidities and for strategies aimed at modulating the microbiome.

Precise pathological classification of non-small cell lung adenocarcinoma and squamous carcinoma based on an integrated platform of targeted metabolome and lipidome

BACKGROUND

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most common subtypes of NSCLC. Despite genetic differences between LUAD and LUSC have been clarified in depth, the metabolic differences of these two subtypes are still unclear.

METHODS

Totally, 128 plasma samples of NSCLC patients were collected before initial treatments, followed by determination of LC-ESI-Q TRAP-MS/MS. Differentially expressed metabolites were screened based on a strict standard.

RESULTS

Based on the integrated platform of targeted metabolome and lipidome, a total of 1141 endogenous metabolites (including 809 lipids) were finally detected in the plasma of NSCLC patients, including 16 increased and 3 decreased endogenous compounds in LUAD group when compared with LUSC group. Thereafter, a logistic regression model integrating four differential metabolites [2-(Methylthio) ethanol, Cortisol, D-Glyceric Acid, and N-Acetylhistamine] was established and could accurately differentiate LUAD and LUSC with an area under the ROC curve of 0.946 (95% CI 0.886-1.000). The cut-off value showed a satisfactory efficacy with 92.0% sensitivity and 92.9% specificity. KEGG functional enrichment analysis showed these differentially expressed metabolites could be further enriched in riboflavin metabolism, steroid hormone biosynthesis, prostate cancer, etc. The endogenous metabolites identified in this study have the potential to be used as novel biomarkers to distinguish LUAD from LUSC.

CONCLUSIONS

Our research might provide more evidence for exploring the pathogenesis and differentiation of NSCLC. This research could promote a deeper understanding and precise treatment of lung cancer.

Alterations of gut microbiota and metabolome with Parkinson's disease

Gut microbiota and amino acids that are one of their metabolites play important roles in the mechanism of pathology of Parkinson's disease (PD). It has been reported that the level of amino acids in vivo participate in neurodegeneration by regulating adaptive immune response, while the current researches on alteration of amino acids in gut microbiota are still insufficient. We hypothesized that alterations in gut microbiota might be accompanied by altered concentrations of amino acids, leading to the occurrence of PD. In this study, we collected stool samples from PD and healthy controls to analyse fecal microbiome and targeted metabolome by 16S ribosomal RNA (16S rRNA) gene sequencing and gas chromatography coupled to mass spectrometry (GC-MS). At the genus level, there was a greater abundance of Alistipes, Rikenellaceae_RC9_gut_group, Bifidobacterium, Parabacteroides, while Faecalibacterium was decreased in fecal samples from PD patients. Moreover, fecal branched chain amino acids (BCAAs) and aromatic amino acids concentrations were significantly reduced in PD patients compared to controls. Our study not only finds the abundance of certain gut microbiota in PD,but also reveals that it is related to BCAAs and aromatic amino acids. These findings are beneficial to identifying new therapeutic targets for PD by regulating diet and/or gut microbiota.

Metabolomics signatures of acutely ill and short-term weight recovered women with anorexia nervosa

Although metabolomics studies are recently spreading and have allowed the characterization of putative biomarkers in many diseases, they are relatively scanty in anorexia nervosa (AN). In this explorative study we analyzed the fecal metabolomics profiles of women with AN in the underweight phase (n = 24) and after short-term weight restoration (n = 16) and compared them with 20 healthy women. An untargeted metabolomic procedure allowed the characterization of 224 metabolites involved in energy, lipid, and amino acid metabolism. A partial least square discriminant analysis identified 14 metabolites with a variable importance in projection score >1.5 that clearly differentiated underweight from weight-restored patients from healthy women. Compared with healthy women, fecal concentrations of valeric acid and 3-methyl,2-ketobutyric acid were increased in both underweight and weight-restored patients; fecal concentrations of propionic acid, stearic acid, linolenic acid, methyl-galactoside, coprosterol, cycloserine, and lauric acid were increased while fecal levels of xylose, fucose, and rhamnose were decreased in underweight patients and normalized after weight-restoration; fecal concentrations of piperine, phenylalanine, butyric acid, and meso-erythritol-1 were decreased while fecal levels of hydroxystearic acid were increased in weight-restored but normal in underweight AN patients. All these changes point to peculiar fecal metabolomics profiles of acute and short-term weight restored AN patients. The value of these changes to improve our understanding of the pathophysiology of AN and to characterize potential biomarker targets for developing new treatment strategies needs further studies to be clarified.

Towards Understanding COVID-19: Molecular Insights, Co-infections, Associated Disorders, and Aging

BACKGROUND

COVID-19 can be related to any diseases caused by microbial infection(s) because 1) co-infection with COVID-19-related virus and other microorganism(s) and 2) because metabolites produced by microorganisms such as bacteria, fungi, and protozoan can be involved in necrotizing pneumonia and other necrotizing medical conditions observed in COVID-19.

OBJECTIVE

By way of illustration, the microbial metabolite of aromatic amino acid tryptophan, a biogenic amine tryptamine inducing neurodegeneration in cell and animal models, also induces necrosis.

METHODS

This report includes analysis of COVID-19 positivity by zip codes in Florida and relation of the positivity to population density, possible effect of ecological and social factors on spread of COVID-19, autopsy analysis of COVID-19 cases from around the world, serum metabolomics analysis, and evaluation of autoantigenome related to COVID-19.

RESULTS

In the present estimations, COVID-19 positivity percent per zip code population varied in Florida from 4.65% to 44.3% (February 2021 data). COVID-19 analysis is partially included in my book Microbial Metabolism and Disease (2021). The autoantigenome related to COVID-19 is characterized by alterations in protein biosynthesis proteins including aminoacyl-tRNA synthetases. Protein biosynthesis alteration is a feature of Alzheimer's disease. Serum metabolomics of COVID-19 positive patients show alteration in shikimate pathway metabolism, which is associated with the presence of Alzheimer's disease-associated human gut bacteria.

CONCLUSION

Such alterations in microbial metabolism and protein biosynthesis can lead to toxicity and neurodegeneration as described earlier in my book Protein Biosynthesis Interference in Disease (2020).

Application of metabolomics in clinical and laboratory gastrointestinal oncology

Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.

The chemopreventive effects of Huangqin-tea against AOM-induced preneoplastic colonic aberrant crypt foci in rats and omics analysis

Despite that colorectal cancer (CRC) is a severe global health problem, effective chemopreventive strategies against CRC are still lacking. Huang-qin tea (HQT), a healthy herbal tea, is prepared from the aerial parts of Scutellaria baicalensis Georgi and has been consumed in China for thousands of years. HQT contains abundant flavonoids, which display potent anticancer effects, but no research studies have investigated the cancer-preventive effects of HQT on CRC in vivo. Here, we found that HQT inhibits azoxymethane-induced aberrant crypt foci (ACF) formation in a preneoplastic colonic ACF rat model. The essential role of the gut microbiota in the chemopreventive effect of HQT on CRC in a pseudo-germ-free rat model was confirmed. Besides, HQT modulates inflammatory cytokine expression by significantly decreasing IL-1β, IL-6, IL-10, and TNF-α expression, and elevating IFN-γ production. 16S rDNA sequencing analysis indicated that HQT regulated the gut microbiota by increasing the abundance of beneficial bacteria (Lachnoclostridium, Alistipes, Roseburia, and Lactococcus) and reducing the levels of Bacteroides, Parasutterella, and unidentified_Clostridiales. Fecal metabolomics showed that HQT modulated the AOM-induced metabolomic disorder, and these altered metabolites were almost involved in the lipid metabolic pathways. The Spearman correlation analysis revealed a correlation between the gut microbiota and fecal metabolites. Collectively, these results suggested that HQT exerted beneficial effects on host health by inhibiting inflammation, and by regulating the gut microbiota profile and certain metabolic pathways. In conclusion, HQT inhibits AOM-induced ACF formation by modulating the gut microbiota composition and improving metabolomic disorders, indicating the potential of HQT as a functional beverage candidate for the prevention and treatment of CRC.

New Plausible Mechanism for Gastric and Colorectal Carcinogenesis: Free Radical-Mediated Acetaldehyde Generation in a Heme/Myoglobin-Linoleate-Ethanol Mixture

Epidemiological studies have revealed that alcohol, red meat, and cooking oil (or linoleate) are risk factors for both gastric and colon cancers. A survey of the mutation spectra of the p53 tumor suppressor gene in these cancers suggested that the types of mutations and the hot spots are similar to those induced by acetaldehyde (AcAld) in an in vitro p53 mutation analysis system. Accordingly, various combinations of possible factors, components, or model compounds were reacted in an emulsion and tested for the generation of AcAld. Efficient AcAld formation was only observed with combinations of three factors, red meat homogenate (or heme/myoglobin), methyl linoleate, and ethanol, but not by any combination of the two. The generated AcAld levels (ca. 500 μM) far exceeded the minimum mutagenic concentration (40-100 μM) obtained using concentrations of meat homogenate (or heme/Mb), linoleate, and ethanol comparable to those in the stomach after an ordinary meal. A mutagenic level of AcAld (75 μM) was also generated with a physiological concentration of ethanol, heme, and linoleate in the colon. As a mechanism, linoleate hydroperoxide formation and its decomposition in the presence of myoglobin (or heme) to generate the OH radical seem to be involved in the ethanol-to-AcAld conversion.

Indolic Structure Metabolites as Potential Biomarkers of Non-infectious Diseases

Interest in indolic structure metabolites, including a number of products of microbial biotransformation of the aromatic amino acid tryptophan, is increasingly growing. The review prepared by a team of authors is based on in-depthscrutiny of data available in PubMed, Scopus, Cyberleninka, Clinical Trials, and Cochrane Library, eventually narrowing the search to a set of keywords such as tryptophan metabolites; plasma metabolomics profiling; metabolomics fingerprinting; gas-, liquid chromatography mass spectrometry; serotonin; melatonin; tryptamine; indoxyl sulfate; indole-3-acetic acid; indole-3-propionic acid; 5-hydroxyindole-3-acetic acid; gut microbiota and microbial metabolites. It provides a summary that outlines the pattern of changes in the level of indolic structure metabolites in a number of diseases and deals with the data from the field of human microbiota metabolites. In modern experimental studies, including the use of gnotobiological (germ-free) animals, it has been convincingly proved that the formation of tryptophan metabolites such as indole-3-acetic acid, indole-3-propionic acid, tryptamine, and indoxyl sulfate is associated with gut bacteria. Attention to some concentration changes of indolic compounds is due to the fact that pronounced deviations and a significant decrease of these metabolites in the blood were found in a number of serious cardiovascular, brain or gastrointestinal diseases. The literature-based analysis allowed the authors to conclude that a constant (normal) level of the main metabolites of the indolic structure in the human body is maintained by a few strict anaerobic bacteria from the gut of a healthy body belonging to the species of Clostridium, Bacteroides, Peptostreptococcus, Eubacteria, etc. The authors focus on several metabolites of the indolic structure that can be called clinically significant in certain diseases, such as schizophrenia, depression, atherosclerosis, colorectal cancer, etc. Determining the level of indole metabolites in the blood can be used to diagnose and monitor the effectiveness of a comprehensive treatment approach.

Pre-Diagnostic Circulating Metabolites and Colorectal Cancer Risk in the Cancer Prevention Study-II Nutrition Cohort

Untargeted metabolomic studies have identified potential biomarkers of colorectal cancer risk, but evidence is still limited and broadly inconsistent. Among 39,239 Cancer Prevention Study II Nutrition cohort participants who provided a blood sample between 1998–2001, 517 newly diagnosed colorectal cancers were identified through 30 June 2015. In this nested case–control study, controls were matched 1:1 to cases on age, sex, race and date of blood draw. Mass spectroscopy-based metabolomic analyses of pre-diagnostic plasma identified 886 named metabolites, after quality control exclusions. Conditional logistic regression models estimated multivariable-adjusted odds ratios (OR) and 95% confidence intervals (CI) for 1 standard deviation (SD) increase in each metabolite with risk of colorectal cancer. Six metabolites were associated with colorectal cancer risk at a false discovery rate < 0.20. These metabolites were of several classes, including cofactors and vitamins, nucleotides, xenobiotics, lipids and amino acids. Five metabolites (guanidinoacetate, 2’-O-methylcytidine, vanillylmandelate, bilirubin (E,E) and N-palmitoylglycine) were positively associated (OR per 1 SD = 1.29 to 1.32), and one (3-methylxanthine) was inversely associated with CRC risk (OR = 0.79, 95% CI, 0.69–0.89). We did not replicate findings from two earlier prospective studies of 250 cases each after adjusting for multiple comparisons. Large pooled prospective analyses are warranted to confirm or refute these findings and to discover and replicate metabolites associated with colorectal cancer risk.

Changes in the Serum Metabolome of Patients Treated With Broad-Spectrum Antibiotics

Background:

The gut microbiome (GMB) generates numerous small chemicals that can be absorbed by the host and variously biotransformed, incorporated, or excreted. The resulting metabolome can provide information about the state of the GMB, of the host, and of their relationship. Exploiting this information in the service of biomarker development is contingent on knowing the GMB-sensitivity of the individual chemicals comprising the metabolome. In this regard, human studies have lagged far behind animal studies. Accordingly, we tested the hypothesis that serum levels of chemicals unequivocally demonstrated to be GMB-sensitive in rodent models would also be affected in a clinical patient sample treated with broad spectrum antibiotics.

Methods:

We collected serum samples from 20 hospitalized patients before, during, and after treatment with broad-spectrum antibiotics. We also collected samples from 5 control patients admitted to the hospital but not prescribed antibiotics. We submitted the samples for a non-targeted metabolomic analysis and then focused on chemicals known to be affected both by germ-free status and by antibiotic treatment in the mouse and/or rat.

Results:

Putative identification was obtained for 499 chemicals in human serum. An aggregate analysis did not show any time x treatment interactions. However, our literature search identified 10 serum chemicals affected both by germ-free status and antibiotic treatment in the mouse or rat. Six of those chemicals were measured in our patient samples and additionally met criteria for inclusion in a focused analysis. Serum levels of 5 chemicals (p-cresol sulfate, phenol sulfate, hippurate, indole propionate, and indoxyl sulfate) declined significantly in our group of antibiotic-treated patients but did not change in our patient control group.

Conclusions:

Broad-spectrum antibiotic treatment in patients lowered serum levels of selected chemicals previously demonstrated to be GMB-sensitive in rodent models. Interestingly, all those chemicals are known to be uremic solutes that can be derived from aromatic amino acids (L-phenylalanine, L-tyrosine, or L-tryptophan) by anaerobic bacteria, particularly Clostridial species. We conclude that judiciously selected serum chemicals can reliably detect antibiotic-induced suppression of the GMB in man and thus facilitate further metabolome-based biomarker development.